Snow Temperature Profiles Research Articles

Operational and experimental snow observation systems in the upper Rofental: data from 2017 to 2023

Abstract. This publication presents a comprehensive hydrometeorological data set for three research sites in the upper Rofental (1891–3772 m a.s.l., Ötztal Alps, Austria) and is a companion publication to a data collection published in 2018. The time series presented here comprise data from 2017 to 2023 and originate from three meteorological and snow hydrological stations at 2737, 2805, and 2919 m a.s.l. The fully equipped automatic weather stations include a specific set of sensors to continuously record snow cover properties. These are automatic measurements of snow depth, snow water equivalent, volumetric solid and liquid water contents, snow density, layered snow temperature profiles, and snow surface temperature. One station is extended by a particular arrangement of two snow depth and water equivalent recording devices to observe and quantify wind-driven snow transport. These devices are installed at nearby wind-exposed and sheltered locations and are complemented by an acoustic-based snow drift sensor. We present data for temperature, precipitation, humidity, wind speed, and radiation fluxes and explore the continuous snow measurements by combined analyses of meteorological and snow data to show typical seasonal snow cover characteristics. The potential of the snow drift observations is demonstrated with examples of measured wind speeds, snow drift rates, and redistributed snow amounts during several blowing snow events. The data complement the scientific monitoring infrastructure in the research catchment and represent a unique time series of high-altitude mountain weather and snow observations. They enable comprehensive insights into the dynamics of high-altitude meteorological and snow processes and are collected to support the scientific community, local stakeholders, and the interested public, as well as operational warning and forecasting services. The data are publicly available from the GFZ Data Services repository: https://doi.org/10.5880/fidgeo.2023.037 (Department of Geography, University of Innsbruck, 2024).

Soot-on-snow experiment: artificial deposition of light-absorbing particles onto snow surfaces in 2018

The absorption of shortwave irradiance in snow depends on the physical properties of snow (e.g., snow grain size and shape, liquid water content, etc.) and light-absorbing particles (LAP). Originating from natural and anthropogenic sources, LAP has been reported to accelerate snowmelt significantly in different regions globally. Yet, our process-level understanding of LAP after deposition onto snow remains rather limited. Here we investigate the impacts of artificial deposition of different LAP onto snow surfaces in an outdoor environment of northern Finland. Following LAP dry deposition into a custom-made tent standing on top of the snowpack, the albedo was followed along with the properties of snow in snow pit measurements throughout the spring season. The results showed that the albedo decay at the end of the season for the different spots were linked to the initial amount and type of LAP that were deposited onto the snowpack. Measured snow temperature profiles from LAP doped snow versus natural reference snow illustrated that the LAP affected snow had higher temperatures in the subsurface snow layers. Collected snow samples analyzed for size distribution of soot particles revealed no apparent agglomeration of soot particles during thaw-freezing events taking place during the experiment. Despite the relatively large perturbation of the experimentally deposited LAP, their impact on the season length was only up to 3 days. Additional experiments are, nevertheless, needed to better constrain the effects of LAP on snow albedo, melt rate, and other associated processes.

Conjoint Inversion of Snow Temperature Profiles from Microwave and Infrared Brightness Temperature in Antarctica

The snow temperature above the ice sheet is one of the basic characteristic parameters of the ice sheet, which plays an important role in the study of the global climate. Because infrared and microwaves with different frequencies have different penetration depths in snow, it is possible to retrieve the snow temperature profiles by combining microwave and infrared brightness temperatures. This paper proposes a conjoint inversion algorithm to retrieve snow temperature profiles by combining multi-frequency microwave brightness temperature (BT) with infrared BT, in which different weight functions of microwave BT at different frequencies are adopted, and the atmosphere influence has also been corrected. The snow temperature profile data are retrieved based on AMSR2 microwave BT data and MODIS infrared BT data in 2017 and 2018, which are evaluated by comparing with the measured snow temperature at Dome-C station. The results confirm that the inverted snow temperature profiles are consistent with the field observation data from the Dome-C station. Multi-frequency microwave brightness temperature can be used to invert the snow temperature profiles; however, the inverted snow surface temperature is more accurate by combining the infrared BT with the microwave BT in the conjoint inversion algorithm.

A Meteorology and Snow Data Set From Adjacent Forested and Meadow Sites at Crested Butte, CO, USA

AbstractWe present meteorology and snow observation data collected at sites in the southwestern Colorado Rocky Mountains (USA) over three consecutive water years with different amounts of snow water equivalent (SWE) accumulation: A year with above average SWE (2019), a year with average SWE (2020), and a year with below average SWE (2021). This data set is distinguished by its emphasis on paired open‐forest sites in a continental snow climate. Approximately once a month during February–May, we collected data from 15 to 20 snow pits and took 8 to 19 snow depth transects. Our sampling sites were in open and adjacent forested areas at 3,100 m and in a lower elevation aspen (3,035 m) and higher elevation conifer stand (3,395 m). In total, we recorded 270 individual snow pit density and temperature profiles and over 4,000 snow depth measurements. These data are complimented by continuous meteorological measurements from two weather stations: One in the open and one in the adjacent forest. Meteorology data—including incoming shortwave and longwave radiation, outgoing shortwave radiation, relative humidity, wind speed, snow depth, and air and infrared surface temperature—were quality controlled and the forcing data were gap‐filled. These data are available to download from Bonner, Smyth, et al. (2022) at https://doi.org/10.5281/zenodo.6618553, at three levels of processing, including a level with downscaled, adjusted precipitation based on data assimilation using observed snow depth and a process‐based snow model. We demonstrate the utility of these data with a modeling experiment that explores open‐forest differences and identifies opportunities for improvements in model representation.

GABLS4 intercomparison of snow models at Dome C in Antarctica

Abstract. The Antarctic plateau, characterized by cold and dry weather conditions with very little precipitation, is mostly covered by snow at the surface. This paper describes an intercomparison of snow models, of varying complexity, used for numerical weather prediction or academic research. The results of offline numerical simulations, carried out during 15 d in 2009, on a single site on the Antarctic plateau, show that the simplest models are able to reproduce the surface temperature as well as the most complex models provided that their surface parameters are well chosen. Furthermore, it is shown that the diversity of the surface parameters of the models strongly impacts the numerical simulations, in particular the temporal variability of the surface temperature and the components of the surface energy balance. The models tend to overestimate the surface temperature by 2–5 K at night and underestimate it by 2 K during the day. The observed and simulated turbulent latent heat fluxes are small, of the order of a few W m−2, with a tendency to underestimate, while the sensible heat fluxes are in general too intense at night as well as during the day. The surface temperature errors are consistent with too large a magnitude of sensible heat fluxes during the day and night. Finally, it is shown that the most complex multilayer models are able to reproduce well the propagation of the daily diurnal wave, and that the snow temperature profiles in the snowpack are very close to the measurements carried out on site.

Meteorological, snow and soil data (2013–2019) from a herb tundra permafrost site at Bylot Island, Canadian high Arctic, for driving and testing snow and land surface models

Abstract. Seasonal snow covers Arctic lands 6 to 10 months of the year and is therefore an essential element of the Arctic geosphere and biosphere. Yet, even the most sophisticated snow physics models are not able to simulate fundamental physical properties of Arctic snowpacks such as density, thermal conductivity and specific surface area. The development of improved snow models is in progress, but testing requires detailed driving and validation data for high Arctic herb tundra sites, which are presently not available. We present 6 years of such data for an ice-wedge polygonal site in the Canadian high Arctic, in Qarlikturvik valley on Bylot Island at 73.15∘ N. The site is on herb tundra with no erect vegetation and thick permafrost. Detailed soil properties are provided. Driving data are comprised of air temperature, air relative and specific humidity, wind speed, shortwave and longwave downwelling radiation, atmospheric pressure, and precipitation. Validation data include time series of snow depth, shortwave and longwave upwelling radiation, surface temperature, snow temperature profiles, soil temperature and water content profiles at five depths, snow thermal conductivity at three heights, and soil thermal conductivity at 10 cm depth. Field campaigns in mid-May for 5 of the 6 years of interest provided spatially averaged snow depths and vertical profiles of snow density and specific surface area in the polygon of interest and at other spots in the valley. Data are available at https://doi.org/10.5885/45693CE-02685A5200DD4C38 (Domine et al., 2021). Data files will be updated as more years of data become available.

Impact of updated radiative transfer scheme in snow and ice in RACMO2.3p3 on the surface mass and energy budget of the Greenland ice sheet

Abstract. Radiative transfer in snow and ice is often not modeled explicitly in regional climate models. In this study, we evaluate a new englacial radiative transfer scheme and assess the surface mass and energy budget for the Greenland ice sheet in the latest version of the regional climate model RACMO2, version 2.3p3. We also evaluate the modeled (sub)surface temperature and melt, as radiation penetration now enables internal heating. The results are compared to the previous model version and are evaluated against stake measurements and automatic weather station data of the K-transect and PROMICE projects. In addition, subsurface snow temperature profiles are compared at the K-transect, Summit, and southeast Greenland. The surface mass balance is in good agreement with observations, with a mean bias of −31 mm w.e. yr−1 (−2.67 %), and only changes considerably with respect to the previous RACMO2 version around the ice margins and near the percolation zone. Melt and refreezing, on the other hand, are changed more substantially in various regions due to the changed albedo representation, subsurface energy absorption, and meltwater percolation. Internal heating leads to higher snow temperatures in summer, in agreement with observations, and introduces a shallow layer of subsurface melt. Hence, this study shows the consequences and necessity of radiative transfer in snow and ice for regional climate modeling of the Greenland ice sheet.

A Lagrangian Snow-Evolution System for Sea-Ice Applications (SnowModel-LG): Part I-Model Description.

A Lagrangian snow‐evolution model (SnowModel‐LG) was used to produce daily, pan‐Arctic, snow‐on‐sea‐ice, snow property distributions on a 25 × 25‐km grid, from 1 August 1980 through 31 July 2018 (38 years). The model was forced with NASA's Modern Era Retrospective‐Analysis for Research and Applications‐Version 2 (MERRA‐2) and European Centre for Medium‐Range Weather Forecasts (ECMWF) ReAnalysis‐5th Generation (ERA5) atmospheric reanalyses, and National Snow and Ice Data Center (NSIDC) sea ice parcel concentration and trajectory data sets (approximately 61,000, 14 × 14‐km parcels). The simulations performed full surface and internal energy and mass balances within a multilayer snowpack evolution system. Processes and features accounted for included rainfall, snowfall, sublimation from static‐surfaces and blowing‐snow, snow melt, snow density evolution, snow temperature profiles, energy and mass transfers within the snowpack, superimposed ice, and ice dynamics. The simulations produced horizontal snow spatial structures that likely exist in the natural system but have not been revealed in previous studies spanning these spatial and temporal domains. Blowing‐snow sublimation made a significant contribution to the snowpack mass budget. The superimposed ice layer was minimal and decreased over the last four decades. Snow carryover to the next accumulation season was minimal and sensitive to the melt‐season atmospheric forcing (e.g., the average summer melt period was 3 weeks or 50% longer with ERA5 forcing than MERRA‐2 forcing). Observed ice dynamics controlled the ice parcel age (in days), and ice age exerted a first‐order control on snow property evolution.

Empirical prediction of snow and land-fast sea ice temperature from surface meteorology on Pistolet Bay, Northern Newfoundland

Abstract The temperature of sea ice and snow cover on the ice surface is a critical component of thermodynamic models of sea ice. Ice temperature is also an important factor in the sea ice strength and therefore in the potential loads the ice can exert on ships, ports, bridges, and offshore structures. The snow layer, while remaining distinct from the sea ice in its physical properties, can become compacted on the ice and contribute to the effective ice thickness and strength. The temperature of sea ice is governed by the atmospheric energy flux balance at the snow surface and the ocean energy flux at the ice base. In addition, snow cover acts as an insulator to sea ice, buffering it against temperature changes due to changes in surface meteorological conditions. The average internal ice temperature and total thickness (through its impact on ice salinity and brine volume) significantly affect ice strength parameters used in the calculation of loads on engineered structures. In February 2017–2019, three field campaigns were carried out on the land-fast ice on Pistolet Bay, north of St. Anthony, Newfoundland in Atlantic Canada. For approximately five days in 2017 and about four days in 2018–2019, surface air temperatures, wind speeds, and snow and ice temperature profiles were continuously measured. Dew point temperatures were obtained from the North American Regional Reanalysis (NARR) dataset. The datasets from the three field campaigns were used to develop empirical linear regression models of snow and ice surface and depth-mean temperatures as a function of the air temperature alone, and of the air and dew point temperatures and wind speed. In this paper, the meteorological data from the three field campaigns are presented, along with the snow and ice thickness and temperature data. The linear models of snow and ice temperature are presented, tested using the observed field data from the Pistolet Bay campaigns, and compared with results obtained from previously published models of snow and sea ice temperature. The results show that the models of snow and ice temperature as a function of the three meteorological variables are modestly more robust than the models as a function of air temperature alone, with R2 values of up to 0.90. The models presented in this paper have potential usefulness in improved prediction of site-specific snow/ice temperatures and associated temperature-dependent ice properties, such as strength at sites where gridded weather data or in-situ meteorological measurements are available, but in-situ snow/ice temperature measurements are nonexistent.

Spatiotemporal variability and decadal trends of snowmelt processes on Antarctic sea ice observed by satellite scatterometers

Abstract. The timing and intensity of snowmelt processes on sea ice are key drivers determining the seasonal sea-ice energy and mass budgets. In the Arctic, satellite passive microwave and radar observations have revealed a trend towards an earlier snowmelt onset during the last decades, which is an important aspect of Arctic amplification and sea-ice decline. Around Antarctica, snowmelt on perennial ice is weak and very different than in the Arctic, with most snow surviving the summer. Here we compile time series of snowmelt onset dates on seasonal and perennial Antarctic sea ice from 1992 to 2014/15 using active microwave observations from the European Space Agency's (ESA) European Remote Sensing (ERS) 1 and 2 missions (ERS-1 and ERS-2), Quick Scatterometer (QSCAT), and Advanced Scatterometer (ASCAT) radar scatterometers. We define two snowmelt transition stages: a weak backscatter rise, indicating the initial warming and destructive metamorphism of the snowpack (pre-melt), followed by a rapid backscatter rise, indicating the onset of thaw–freeze cycles (snowmelt). Results show large interannual variability, with an average pre-melt onset date of 29 November and melt onset of 10 December, respectively, on perennial ice, without any significant trends over the study period, consistent with the small trends of Antarctic sea-ice extent. There was a latitudinal gradient from early snowmelt onsets in mid-November in the northern Weddell Sea to late (end of December) or even absent snowmelt conditions in the southern Weddell Sea. We show that QSCAT Ku-band-derived (13.4 GHz signal frequency) pre-melt and snowmelt onset dates are earlier by 20 and 18 d, respectively, than ERS and ASCAT C-band-derived (5.6 GHz) dates. This offset has been considered when constructing the time series. Snowmelt onset dates from passive microwave observations (37 GHz) are later by 14 and 6 d than those from the scatterometers, respectively. Based on these characteristic differences between melt onset dates observed by different microwave wavelengths, we developed a conceptual model which illustrates how the seasonal evolution of snow temperature profiles may affect different microwave bands with different penetration depths. These suggest that future multi-frequency active and passive microwave satellite missions could be used to resolve melt processes throughout the vertical snow column of thick snow on perennial Antarctic sea ice.

Observaciones del manto de nieve durante una circunnavegación del casquete de hielo de Groenlandia (primavera de 2014)

Este trabajo describe las características del manto de nieve de Groenlandia a partir de datos recogidos durante la circunnavegación del casquete de hielo de Groenlandia durante la primavera de 2014. La base de datos incluye medidas de espesor de nieve en parcelas de 100 m2, y datos de densidad y de temperatura del manto de nieve a un metro de profundidad medido en 25 puntos a lo largo del trayecto de 4301 kilómetros realizados durante 49 días. Además, se midió la temperatura y densidad de la nieve a intervalos de 10 centímetros en ocho perfiles de un metro de profundidad. Esta información puede resultar de interés para el estudio del manto de nieve en está remota región, así como proporcionar información de utilidad para modelizadores de la atmósfera y procesos criosféricos.El espesor de nieve excedió con frecuencia los 4 metros de profundidad, si bien no se pudo superar en ocasiones una capa de hielo localizada aproximadamente a 70 centímetros de profundidad. Esta capa de hielo está posiblemente asociada al evento de fusión de julio de 2012 que afectó a un 98% de la superficie del casquete de hielo. Fuera de la zona de acumulación, el espesor de nieve medido fue bastante constante en torno a 1.5 metros. La temperatura del manto de nieve osciló entre los -20º y -10ºC en la mayor parte de los casos, y ha mostrado una correlación muy alta con la temperatura del aire de los 15 días anteriores a la medida. La densidad del manto de nieve fue bastante homogénea en la mayoría de los puntos de medición, oscilando entre los 320 to 390 kg m–3. Los perfiles de temperatura y densidad informan de un manto de nieve progresivamente más frío en las capas más profundas, observándose los mayores (menores) gradientes en las zonas más cálidas (frías). Se detecta un tramo de mayor densidad en torno a los 60-80 cm, coincidiendo con la capa de hielo previamente detectada. En las zonas menos frías del recorrido, se ha encontrado una capa más densa relativamente próxima a la superficie, posiblemente debida a un evento de fusión más reciente.

A 1.5‐D anisotropic sigma‐coordinate thermal stress model of landlocked sea ice in the Canadian Arctic Archipelago

AbstractWe present a 1.5‐D thermal stress model that takes into account the effect of land confinement, which causes anisotropy in thermal stresses. To this end, we fix the total strain in the direction perpendicular to the coastline to its value at landlocked ice onset. This prevents thermal expansion in the direction perpendicular to the coastline and therefore induces larger thermal stresses in this direction. The simulated stresses best match the observations, when a Young's Modulus of 0.5 GPa and a relaxation time constant of 8 days are used. This simulation gives root‐mean‐square errors of 13.0 and 13.1 kPa (∼15%) in the major and minor principal stresses, respectively. The simulated anisotropic component of thermal stress also generally agrees with observations. The optimal Young's Modulus is in the low range of reported values in the literature, and the optimal relaxation time constant (8 days) is larger than the largest relaxation time constant reported in the literature (5 days). A series of experiments are done to examine the model sensitivity to vertical resolution, snow cover, and the parameterizations of Young's Modulus and viscous creep. Results show that a minimum of one and three layers in the snow and ice, respectively, is required to simulate the thermal stresses within 15% error of the value assessed with the higher‐resolution control simulation. This highlights the importance of resolving the internal snow and ice vertical temperature profile in order to properly model the thermal stresses of sea ice.

Numerical simulation of extreme snowmelt observed at the SIGMA-A site, northwest Greenland, during summer 2012

Abstract. The surface energy balance (SEB) from 30 June to 14 July 2012 at site SIGMA (Snow Impurity and Glacial Microbe effects on abrupt warming in the Arctic)-A, (78°03' N, 67°38' W; 1490 m a.s.l.) on the northwest Greenland Ice Sheet (GrIS) was investigated by using in situ atmospheric and snow measurements as well as numerical modeling with a one-dimensional multi-layered physical snowpack model called SMAP (Snow Metamorphism and Albedo Process). At SIGMA-A, remarkable near-surface snowmelt and continuous heavy rainfall (accumulated precipitation between 10 and 14 July was estimated to be 100 mm) were observed after 10 July 2012. Application of the SMAP model to the GrIS snowpack was evaluated based on the snow temperature profile, snow surface temperature, surface snow grain size, and shortwave albedo, all of which the model simulated reasonably well. Above all, the fact that the SMAP model successfully reproduced frequently observed rapid increases in snow albedo under cloudy conditions highlights the advantage of the physically based snow albedo model (PBSAM) incorporated in the SMAP model. Using such data and model, we estimated the SEB at SIGMA-A from 30 June to 14 July 2012. Radiation-related fluxes were obtained from in situ measurements, whereas other fluxes were calculated with the SMAP model. By examining the components of the SEB, we determined that low-level clouds accompanied by a significant temperature increase played an important role in the melt event observed at SIGMA-A. These conditions induced a remarkable surface heating via cloud radiative forcing in the polar region.

Verification of Snow-depth Measurement Technique Based on Snow Temperature Profile

Verification of Snow-depth Measurement Technique Based on Snow Temperature Profile

Analysis of continuous snow temperature profiles from automatic weather stations in Aosta Valley (NW Italy): Uncertainties and applications

Continuous measurements of snow temperature profiles may be a valuable tool to investigate occurrence and persistence of thermal conditions promoting the strengthening or weakening of the snow structure, with potentially important consequences on avalanche release. In this paper, automatic measurements of snow temperature profiles are analyzed based on an extensive dataset (67site-years) in Aosta Valley, Italy. The aims of this work are: (1) to highlight issues and uncertainties in the data and show appropriate data filtering that may be implemented by similar measurement networks; (2) to assess the impact of data filtering on temperature gradient calculation, and (3) to quantitatively describe the occurrence and duration of strong temperature gradients at the base of the snowpack and close to the snow surface that may lead to the formation of weak layers.Three main sources of uncertainty were identified and corrected: (1) errors in the data logging; (2) drifts in temperature measurements; and (3) a large bias in spring measurements due to snow melting and lack of contact between the sensor and the surrounding snow.We estimated that strong temperature gradients may account for as much as 25% of total gradients and the duration of these may be as long as 35days. The frequency of strong gradients was significantly higher at the snow surface than at the snowpack base. Hence, the importance of surface faceted crystals as potential weak layers in alpine snowpacks is highlighted.

An 18-yr long (1993–2011) snow and meteorological dataset from a mid-altitude mountain site (Col de Porte, France, 1325 m alt.) for driving and evaluating snowpack models

Abstract. A quality-controlled snow and meteorological dataset spanning the period 1 August 1993–31 July 2011 is presented, originating from the experimental station Col de Porte (1325 m altitude, Chartreuse range, France). Emphasis is placed on meteorological data relevant to the observation and modelling of the seasonal snowpack. In-situ driving data, at the hourly resolution, consist of measurements of air temperature, relative humidity, windspeed, incoming short-wave and long-wave radiation, precipitation rate partitioned between snow- and rainfall, with a focus on the snow-dominated season. Meteorological data for the three summer months (generally from 10 June to 20 September), when the continuity of the field record is not warranted, are taken from a local meteorological reanalysis (SAFRAN), in order to provide a continuous and consistent gap-free record. Data relevant to snowpack properties are provided at the daily (snow depth, snow water equivalent, runoff and albedo) and hourly (snow depth, albedo, runoff, surface temperature, soil temperature) time resolution. Internal snowpack information is provided from weekly manual snowpit observations (mostly consisting in penetration resistance, snow type, snow temperature and density profiles) and from a hourly record of temperature and height of vertically free ''settling'' disks. This dataset has been partially used in the past to assist in developing snowpack models and is presented here comprehensively for the purpose of multi-year model performance assessment. The data is placed on the PANGAEA repository (http://dx.doi.org/10.1594/PANGAEA.774249) as well as on the public ftp server ftp://ftp-cnrm.meteo.fr/pub-cencdp/.

Thermal diffusivity of seasonal snow determined from temperature profiles

Thermal diffusivity of snow is an important thermodynamic property associated with key hydrological phenomena such as snow melt and heat and water vapor exchange with the atmosphere. Direct determination of snow thermal diffusivity requires coupled point measurements of thermal conductivity and density, which continually change due to snow metamorphism. Traditional methods for determining these two quantities are generally limited by temporal resolution. In this study we present a method to determine the thermal diffusivity of snow with high temporal resolution using snow temperature profile measurements. High resolution (between 2.5 and 10cm at 1min) temperature measurements from the seasonal snow pack at the Plaine-Morte glacier in Switzerland are used as initial conditions and Neumann (heat flux) boundary conditions to numerically solve the one-dimensional heat equation and iteratively optimize for thermal diffusivity. The implementation of Neumann boundary conditions and a t-test, ensuring statistical significance between solutions of varied thermal diffusivity, are important to help constrain thermal diffusivity such that spurious high and low values as seen with Dirichlet (temperature) boundary conditions are reduced. The results show that time resolved thermal diffusivity can be determined from temperature measurements of seasonal snow and support density-based empirical parameterizations for thermal conductivity.

Variations of snow temperature and their influence on snow cover physical parameters in the Western Tianshan Mountains, China

This article discussed about snow temperature variations and their impact on snow cover parameters. Automatic temperature recorders were used to sample at 10-minute intervals at the Tianshan Station for Snow-cover and Avalanche Research, Chinese Academy of Sciences. 10-layer snow temperature and the snow cover parameters were measured by the snow property analyzer (Snow Fork) in its Stable period, Interim period and Snow melting period. Results indicate that the amplitude of the diurnal fluctuation in the temperature during Snow melting period is 1.62 times greater than that during Stable period. Time up to the peak temperature at the snow surface lags behind the peak solar radiation by more than 2.5 hours, and lags behind the peak atmospheric temperature by more than 0.2 hours during all three periods. The optimal fitted function of snow temperature profile becomes more complicated from Stable period to Snow melting period. 22 h temperature profiles in Stable period are the optimal fitted by cubic polynomial equation. In Interim period and Snow melting period, temperature profiles are optimal fitted by exponential equation between sunset and sunrise, and by Fourier function when solar radiation is strong. The vertical gradient in the snow temperature reaches its maximum value at the snow surface for three periods. The peak of this maximum value occurs during Stable period, and is 4.46 times greater than during Interim period. The absolute value of temperature gradient is lower than 0.1A degrees C cm(-1) for 30 cm beneath snow surface. Snow temperature and temperature gradient in Stable period similar to Interim period indirectly cause increase (decrease) of snow density mainly by increasing (decreasing) permittivity. While it dramatically increases its water content to change its permittivity and snow density in Snow melting period.

Measurement of Temperature Gradient in Seasonal Snowpack using Improved Automated Temperature Profiler

Snow temperature profile is an important input parameter for assessing the thermal state of snowpack. The individual layers of a snowpack can be weakened by various metamorphic processes occurring due to temperature gradient present within the snowpack and may result in avalanche hazard. The profile of snowpack temperatures also affects snowmelt runoff magnitude and timing, and hence, may cause wet avalanches. The design of an automated instrument for the measurement of snow temperature profiles in a snowpack is discussed. The instrument was designed to operate in stand-alone mode and is capable of measuring and logging data of twelve vertical temperature-sensing probes throughout the winter. It is suitable for measurement of snow temperature profiles in areas which are inaccessible to human during winters. The design considerations, field deployment, results, and future scope of work for the instrument is discussed in detail. Defence Science Journal, 2011, 61(6), pp.554-558 , DOI:http://dx.doi.org/10.14429/dsj.61.379

Modeling time series of microwave brightness temperature in Antarctica

AbstractThis paper aims to interpret the temporal variations of microwave brightness temperature (at 19 and 37GHz and at vertical and horizontal polarizations) in Antarctica using a physically based snow dynamic and emission model (SDEM). SDEM predicts time series of top-of-atmosphere brightness temperature from widely available surface meteorological data (ERA-40 re-analysis). To do so, it successively computes the heat flux incoming the snowpack, the snow temperature profile, the microwaves emitted by the snow and, finally, the propagation of the microwaves through the atmosphere up to the satellite. Since the model contains several parameters whose value is variable and uncertain across the continent, the parameter values are optimized for every 50 km × 50 km pixel. Simulation results show that the model is inadequate in the melt zone (where surface melting occurs on at least a few days a year) because the snowpack structure and its temporal variations are too complex. In contrast, the accuracy is reasonably good in the dry zone and varies between 2 and 4 K depending on the frequency and polarization of observations and on the location. At the Antarctic scale, the error is larger where wind is usually stronger, suggesting either that meteorological data are less accurate in windy regions or that some neglected processes (e.g. windpumping, surface scouring) are important. At Dome C, in calm conditions, a detailed analysis shows that most of the error is due to inaccuracy of the ERA-40 air temperature (∼2 K). Finally, the paper discusses the values of the optimized parameters and their spatial variations across the Antarctic.